The subject matter described herein relates generally to processing three-dimensional (3D) imaging datasets, and more particularly, to a method and apparatus for navigating, segmenting, and extracting a 3D image dataset.
Automated segmentation methods are commonly used to outline objects in volumetric image data. Various methods are known that are suitable for 3D segmentation. Most of the segmentation methods rely upon deforming an elastic model towards an edge or edges in the volumetric image data. Accurate segmentation of large quantities of images in a clinical application is often difficult to accomplish because of the complex and varied anatomy of the patient, image inhomogeneity, partial volume effect, and/or motion related imaging artifacts. As a result, automatic image segmentation algorithms are typically adjusted using manual editing techniques implemented by the operator. Manual editing is typically performed on a slice-by-slice and a pixel-by-pixel basis after the automatic segmentation algorithm is completed. Thus, because a 3D image dataset may include thousands of 2D slices, the time required for an operator to perform manual editing is often time consuming.
Moreover, manual editing of the segmentation results is often difficult when the segmentations results must have adequate precision to support clinical decision making. Therefore, because manually editing of the segmentation results is often time consuming, medical imaging applications that do not include highly accurate automatic image segmentation algorithms may not be useful in a clinical setting.